SoroushVahidi/scidocs huggingface dataset:
Security in Infancy , Childhood , and Adulthood : A Move to the Level of Representation
(2007)

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Statecharts: A Visual Formalism for Complex Systems
(1987)

We present a broad extension of the conventional formalism of state machines and state diagrams, that is relevant to the specification and design of complex discrete-event systems, such as multi-computer real-time systems, communication protocols and digital control units. Our diagrams, which we call statecharts, extend conventional state-transition diagrams with essentially three elements, dealing, respectively, with the notions of hierarchy, concurrency and communication. These transform the language of state diagrams into a highly structured and economical description language. Statecharts are thus compact and expressiv-small diagrams can express complex behavior-as well as compositional and modular. When coupled with the capabilities of computerized graphics, statecharts enable viewing the description at different levels of detail, and make even very large specifications manageable and comprehensible. In fact, we intend to demonstrate here that statecharts counter many of the objections raised against conventional state diagrams, and thus appear to render specification by diagrams an attractive and plausible approach. Statecharts can be used either as a stand-alone behavioral description or as part of a more general design methodology that deals also with the system’s other aspects, such as functional decomposition and data-flow specification. We also discuss some practical experience that was gained over the last three years in applying the statechart formalism to the specification of a particularly complex system.
Performance Evaluation of a High-Power-Density PMASynRM With Ferrite Magnets
(2011)

Although motors that use rare-earth permanent magnets (PMs) typically exhibit high performance, high costs and concerns about the stability of raw material supplies are leading to their decreased production. However, the performance of such motors is not easily matched without the use of rare-earth PMs. This paper proposes and examines a PM-assisted synchronous reluctance motor with ferrite magnets that has the same power density as rare-earth PM synchronous motors. A suitable rotor structure for high torque and high power is discussed with respect to the demagnetization of ferrite magnets and the mechanical strength.
Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction
(2013)

Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.
A comparative assessment of classification methods
(2003)

Classification systems play an important role in business decision-making tasks by classifying the available information based on some criteria. The objective of this research is to assess the relative performance of some well-known classification methods. We consider classification techniques that are based on statistical and AI techniques. We use synthetic data to perform a controlled experiment in which the data characteristics are systematically altered to introduce imperfections such as nonlinearity, multicollinearity, unequal covariance, etc. Our experiments suggest that data characteristics considerably impact the classification performance of the methods. The results of the study can aid in the design of classification systems in which several classification methods can be employed to increase the reliability and consistency of the classification. D 2002 Elsevier Science B.V. All rights reserved.
Islamic star patterns from polygons in contact
(2005)

We present a simple method for rendering Islamic star patterns based on Hankin’s “polygons-in-contact” technique. The method builds star patterns from a tiling of the plane and a small number of intuitive parameters. We show how this method can be adapted to construct Islamic designs reminiscent of Huff’s parquet deformations. Finally, we introduce a geometric transformation on tilings that expands the range of patterns accessible using our method. This transformation simplifies construction techniques given in previous work, and clarifies previously unexplained relationships between certain classes of star patterns.
Exact clothing retrieval approach based on deep neural network
(2016)

In recent years, the trend of shopping for clothing on an onscreen character has been raising. Unfortunately, clothes that appear on screen could be difficult to track down, much less to purchase directly. It is a challenging task to find out exactly what the clothing in a TV show is from massive amounts of clothing items in online clothing retails due to the clearly noticeable visual discrepancies of the same clothing in video and in online shop photos. In order to tackle the problem, we adopt the deep neural network to learn recognizable clothing-specific feature for finding the exact clothing item in the video scenario among a large collection of online shop images. The experiments shows the clothing-specific feature is more effective in retrieval task and color filtering improve the retrieval performance.
Multi-objective optimisation algorithms for GIS-based multi-criteria decision analysis : an application for evacuation planning
(2016)

Geographic Information Systems have gradually acquired greater relevance as tools to support decision-making processes, and during the last decades they have been used in conjunction with Multi-Criteria Decision Analysis techniques (GIS-MCDA) to solve real-world spatial problems. GIS-MCDA can be generally divided in two main approaches: Multi-Attribute and MultiObjective techniques. Until now most of the applications of GIS-MCDA have been focused only on using the multi-attribute approach, and less than 10% of the research has been related to a specific type of multi-objective technique: the use of heuristic/meta-heuristic algorithms. The present study explores how different heuristic/meta-heuristic methods perform on solving a spatial multi-objective optimisation problem. To achieve this, four algorithms representing different types of heuristics methods were implemented, and applied to solve the same spatial multi-objective optimisation problem related with an evacuation planning situation. The implemented algorithms were Standard Particle Swarm Optimisation (SPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi-Objective Simulated Annealing (AMOSA) and Multi-Objective Grey Wolf Optimiser (MOGWO). The results show that the four algorithms were effective on solving the given problem, although in general AMOSA and MOGWO had a higher performance for the evaluated aspects (number of solutions, effectiveness of the optimisation, diversity, execution time and repeatability). However, the differences in the results obtained from each algorithm were not clear enough to state that one type of heuristic is superior than others. Since AMOSA and MOGWO are the most recent algorithms among the implemented ones, they include several improvements achieved by the latest research, and their superior performance could be connected to these improvements more than to the specific type of algorithms they belong to. Further research is suggested to explore the suitability of these methods for many-objectives spatial problems, to consider the temporal variability and dynamism of real-world situations, to create a standard set of algorithms to be used for benchmarking, and to integrate them with the currently available GIS-MCDA tools. Despite this, from the performed research it is possible to conclude that heuristics methods are reliable techniques for solving spatial problems with multiple and conflictive objectives, and future research and practical implementations in this field can strengthen the capacities of GIS as a multi-criteria decisionmaking support tool.
Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity
(2015)

The fingerprinting location method is commonly used in WLAN indoor positioning system. Device diversity (DD) which leads to Received Signal Strength (RSS) value difference between the users' device and the reference device is becoming an increasingly important factor impacting the positioning accuracy. Thus, the device diversity is a key problem gained more and more attention in fingerprinting location system recently, which introduces many uncertainties to the positioning result. Traditionally, the Euclidean distance is widely adopted in fingerprinting method. However, when encountering with RSS value difference caused by device diversity, the localization performance is degraded significantly. Due to this problem, our paper proposes a method employing cosine similarity instead of the Euclidean distance to improve the positioning accuracy about 13.15% higher within 2 meters when device diversity exists in the positioning. The experiment results show that the proposed method presents a good performance without the expenses of computation caused by calibration method which is employed in many previous works.
Design comparison of NdFeB and ferrite radial flux magnetic gears
(2016)

Magnetic gears promise the benefits of mechanical gears with added advantages from contactless power transfer. Although most literature focuses on minimizing the size of magnetic gears, their material costs must also be reduced to achieve economic feasibility. This work compares the active material costs of NdFeB and ferrite radial flux magnetic gears with surface permanent magnets through a parametric 2D and 3D finite element analysis (FEA) study. Differences in optimal design trends such as pole count and magnet thicknesses are illustrated for the two materials. The results demonstrate that, for most historical price combination scenarios, NdFeB gear designs are capable of achieving lower active material costs than ferrite gear designs, and they are always capable of achieving much higher torque densities. Based on the selected design constraints, relative to a nominal ferrite cost of $10/kg, NdFeB must cost more than $90/kg before ferrite is cost competitive. Additionally, contour plots are provided to show the impact of material price rate variation on the cost break points.
iPCA : An Interactive System for PCA-based Visual Analytics
(2008)

Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a “black box” operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT’s Interactive Data Analysis. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants’ accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.
Fusion of time-of-flight depth and stereo for high accuracy depth maps
(2008)

Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturerpsilas calibration. We show that these techniques lead to improved accuracy and robustness.
An adaptive and adaptable learning platform with realtime eye-tracking support: lessons learned
(2014)

For a learning platform to be adapti ve, it needs to monitor the user in r ealtime. At the ScienceCampus Tübingen, we conduct empirical research on learning strategies, in particular with regard to system adaptivity. Our experimental Webbased adaptiv e multimedia learning environment is able to communicate with an eyetracking system in real-time, and l inks gaze data to browser-based learning content. By processing simple evaluation scripts in the browser, we can control the learning process by adapting the learning content in response to predefined conditions and user inputs. The experimental system serves as a basis for a series of psyc hologi al studies in the learning context. We have already conducted some studies with our experimental learning platform, but its devel opment is still ongoing. In particular, we are adding new feature s required for future studies. In this paper, we desc ribe the actual state of our experimental learning environment, lessons learned from its a pplication in the studies, and our plans for future devel opment.
Evaluation of 3D Feature Descriptors for Multi-modal Data Registration
(2013)

We propose a framework for 2D/3D multi-modal data registration and evaluate 3D feature descriptors for registration of 3D datasets from different sources. 3D datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2D images are converted into 3D structure by stereo or multiview reconstruction techniques and registered to a unified 3D domain with other datasets in a 3D world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3D feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3D feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration.
2005: Tutorial on Agent-Based Modeling and Simulation
(2005)

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agentbased applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.
A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data
(2009)

Fusion of hard data with soft data is an issue that has attracted recent attention. An effective fusion strategy requires an analytical framework that can capture the uncertainty inherent in hard and soft data. For instance, computational linguistic parsing of text-based data generates logical propositions that inherently possess significant semantic ambiguity. An effective fusion framework must exploit the respective advantages of hard and soft data while mitigating their particular weaknesses. In this paper, we describe a Dempster-Shafer theoretic approach to hard and soft data fusion that relies upon the novel conditional approach to updating. The conditional approach engenders a more flexible method that allows for tuning and adapting update strategies. When computational complexity concerns are taken into account, it also provides guidance on how evidence could be ordered for updating. This has important implications in working with models that convert propositional logic statements from text into Dempster-Shafer theoretic form.
Guidelines for critique of a research report.
(1992)

Before findings reported in a research article are used to change clinical practice, the research must be evaluated for evidence of credibility, integrity, and potential for replication. This paper provides guidelines for use in evaluating deductive, empirical research. The standards presented in this paper are derived from the tenets of the scientific method, measurement theory, statistical principles, and research ethics. The guidelines may be used to evaluate each section of a research report, from the title to the interpretation of findings.
High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications
(2015)

This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.
Effects of small-sided games on physical conditioning and performance in young soccer players.
(2009)

The purpose of this study was to examine, first, the movement actions performed during two different small-sided games and, second, their effects on a series of field endurance and technical tests. Thirty-four young soccer players (age: 13 ± 0.9 yrs; body mass: 62.3 ± 15.1 kg; height: 1.65 ± 0.06 m) participated in the study. Small-sided games included three-a-side (3 versus 3 players) and six-a-side (6 versus 6 players) games consisting of 10 bouts of 4 min duration with 3 min active recovery between bouts. Soccer player performance was evaluated using five field tests: a) 30m sprint, b) throw-in for distance, c) Illinois Agility Test, d) dribbling the ball and e) horizontal jump before, in the middle and after the implementation of both game situations. Heart rate was monitored during the entire testing session. Each game was also filmed to measure soccer movements within the game. The ANOVA analysis indicated that the three-a- side games displayed significantly higher heart rate values compared with the six-a-side games (p < 0.05). The number of short passes, kicks, tackles, dribbles and scoring goals were significantly higher during the three-a-side compared with the six-a-side game condition (p < 0. 05) while players performed more long passes and headed the ball more often during the six-a-side (p < 0.05). After the three-a-side games, there was a significant decline in sprint and agility performance (p < 0.05), while after both game conditions significant alterations in the throw-in and the horizontal jump performance were observed (p < 0.05). The results of the present study indicated that three-a-side games provide higher stimulus for physical conditioning and technical improvement than six-a-side games and their use for training young soccer players is recommended. Key pointsThree-a-side games display higher HR compared with six-a-side games.In the three-a-side games players performed more short passes, kicks, dribbles, tackles and scored more goals compared with the six-a-side games.Impairment in endurance and field test performance was observed mainly after three-a-side games.The use of the three-a-side games to develop physical fitness and technique in young soccer players is recommended.
Modular Monad Transformers
(2009)

During the last two decades, monads have become an indispensable tool for structuring functional programs with computational effects. In this setting, the mathematical notion of a monad is extended with operations that allow programmers to manipulate these effects. When several effects are involved, monad transformers can be used to build up the required monad one effect at a time. Although this seems to be modularity nirvana, there is a catch: in addition to the construction of a monad, the effect-manipulating operations need to be lifted to the resulting monad. The traditional approach for lifting operations is nonmodular and ad-hoc. We solve this problem with a principled technique for lifting operations that makes monad transformers truly modular.